WO2011123772A2 - Procédé et système de caractérisation de lésions en plaques - Google Patents

Procédé et système de caractérisation de lésions en plaques Download PDF

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WO2011123772A2
WO2011123772A2 PCT/US2011/030928 US2011030928W WO2011123772A2 WO 2011123772 A2 WO2011123772 A2 WO 2011123772A2 US 2011030928 W US2011030928 W US 2011030928W WO 2011123772 A2 WO2011123772 A2 WO 2011123772A2
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patient
risk
components
image
plaque
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PCT/US2011/030928
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WO2011123772A3 (fr
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William S. Kerwin
Hui Hu
Dongxiang Xu
Michael George Hartmann
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Vpdiagnostics, Inc.
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Publication of WO2011123772A2 publication Critical patent/WO2011123772A2/fr
Publication of WO2011123772A3 publication Critical patent/WO2011123772A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/563—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution of moving material, e.g. flow contrast angiography
    • G01R33/5635—Angiography, e.g. contrast-enhanced angiography [CE-MRA] or time-of-flight angiography [TOF-MRA]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/174—Segmentation; Edge detection involving the use of two or more images
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Detecting organic movements or changes, e.g. tumours, cysts, swellings
    • A61B8/0891—Detecting organic movements or changes, e.g. tumours, cysts, swellings for diagnosis of blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5215—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data
    • A61B8/5223—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving processing of medical diagnostic data for extracting a diagnostic or physiological parameter from medical diagnostic data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/483—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy
    • G01R33/4833—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy using spatially selective excitation of the volume of interest, e.g. selecting non-orthogonal or inclined slices
    • G01R33/4835—NMR imaging systems with selection of signals or spectra from particular regions of the volume, e.g. in vivo spectroscopy using spatially selective excitation of the volume of interest, e.g. selecting non-orthogonal or inclined slices of multiple slices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R33/00—Arrangements or instruments for measuring magnetic variables
    • G01R33/20—Arrangements or instruments for measuring magnetic variables involving magnetic resonance
    • G01R33/44—Arrangements or instruments for measuring magnetic variables involving magnetic resonance using nuclear magnetic resonance [NMR]
    • G01R33/48—NMR imaging systems
    • G01R33/54—Signal processing systems, e.g. using pulse sequences ; Generation or control of pulse sequences; Operator console
    • G01R33/56—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution
    • G01R33/5602—Image enhancement or correction, e.g. subtraction or averaging techniques, e.g. improvement of signal-to-noise ratio and resolution by filtering or weighting based on different relaxation times within the sample, e.g. T1 weighting using an inversion pulse
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20101—Interactive definition of point of interest, landmark or seed
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    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment

Definitions

  • This disclosure relates generally to methods for assessing a patient's risk associated with atherosclerosis and, more particularly, to clinically efficient methods for characterizing such risks.
  • Atherosclerosis cardiovascular disease resulting from atherosclerosis is a leading cause of mortality and morbidity worldwide.
  • the decisive factor determining increased risk for atherosclerotic plaque to cause clinical events is plaque composition and morphology rather than the degree of luminal narrowing as measured by angiography.
  • Atherosclerosis is a form of arteriosclerosis that is characterized by the deposition of plaques containing cholesterol and lipids on the innermost layer of the walls of arteries.
  • the condition usually affects large- and medium-sized arteries.
  • plaque deposits can significantly reduce the blood's flow through an artery, the more serious risk is generally associated with the instigation of an acute clinical event through plaque rupture and thrombosis.
  • serious damage can occur if an arterial plaque deposit becomes fragile and ruptures, fissures, or ulcerates. Plaque rupture, fissure, or ulcer can cause blood clots to form that block or occlude blood flow and/or break off and travel to other parts of the body.
  • the presence and extent of plaque build up in an individual's arteries can be detected using a variety of techniques that are well known in the field including, for example, magnetic resonance imaging ("MRI”), computed tomography (“CT”), X- ray angiography, and ultrasound.
  • MRI magnetic resonance imaging
  • CT computed tomography
  • X- ray angiography X- ray angiography
  • ultrasound ultrasound
  • Various methods have been devised for assessing an individual's risk of a clinically significant event such as a stroke or heart attack related to atherosclerotic deposits in an individual's arteries based on the data obtained by these techniques.
  • the present disclosure relates generally to a system and method for identification and delineation of clinically relevant features, such as necrotic cores and calcification regions.
  • at least two types of information e.g., intensity and morphology
  • in vivo and imaging such as magnetic resonance imaging (MRI)
  • MRI magnetic resonance imaging
  • each subset (such as a pixel) of the image is first assigned a set scores based on at least two attributes, such as intensity (“intensity score”) and relative position of the subset (“morphology score"); the boundaries delineating each type of relevant feature are automatically calculated based on the scores of the subsets.
  • a further aspect of the present disclosure relates to assessing the risk of a clinically significant event by multiple assessment methods.
  • a patient's risk for stroke may be first assessed based on the degree of stenosis of the carotid artery. If the patient is deemed to suffer from severe stenosis, surgical intervention (including, e.g., carotid endarterectomy ("CEA") and stenting) or other appropriate treatments for reducing or eliminating stroke risks may be indicated; if the stenosis is deemed moderate, a second, more precise method is used to assess the risk. The second method can be, for example, based on the plaque composition, morphology, and/or status.
  • CEA carotid endarterectomy
  • a mandatory sequence may be the following: (a) selecting MRI image sequences as bases for plaque feature characterization and/or risk assessment; (b) identifying and marking the blood vessel boundaries; (c) aligning (registering) the series of images chosen in (a) with each other; (d) delineating plaque regions; and (e) analysis based on the result of the previous steps.
  • Figure 1 is a schematic representation of a portion of a typical carotid artery
  • Figure 2 is a schematic sketch of a magnetic resonance image of a transverse cross-section through section 2-2 of the external carotid artery shown in Figure. 1 ;
  • Figure 3 is a schematic diagram of an example plaque feature
  • Figure 4 is a schematic illustration of an example configuration of the local computer device 400 in Figure 3.
  • Figure 5 is a flow chart showing an example process for plaque feature characterization and/or risk assessment in one aspect of this disclosure.
  • Figure 6(a) is a schematic illustration of one series of slices (solid straight lines) imaged at a particular contrast weighing (e.g., Tl -weighted).
  • Figure 6(b) is a schematic illustration of a different series of slices (solid straight lines) from those shown in Figure 6(a) imaged at a different particular contrast weighing (e.g., time-of-flight-weighted).
  • the dashed lines denote the locations of the images calculated by interpolating the image data from the slices marked by the solid lines.
  • At least a subset (A, B and C) of the images in Figure 6(a) are in longitudinal alignment with at least a subset (D, E and F, respectively) of the interpolated images (dashed lines).
  • Figure 7 shows an example saggital image of an external carotid artery near a bifurcation.
  • the superimposed straight line marks the bifurcation 710.
  • Figures 8(a), (b), (c) and (d) are a set of four example MRI images that are simultaneously displayed on a display device of the plaque feature characterization and/or risk assessment system according to one aspect of this disclosure.
  • the four images are longitudinally aligned with each other, all being from the slice at the bifurcation marked in Figure 7, but have mutually different contrast weighings, respectively.
  • Figure 9 shows an example of the deterministic segmentation algorithm applied to phantom images with three contrast weightings (top row) according to one aspect of the disclosure.
  • the intensity score for calcification shows a bright spot corresponding to the dark region in all contrast weightings.
  • the intensity score for core shows a bright spot corresponding to the region that is bright on T1W and relatively dark on T2W.
  • morphology score (middle column)
  • This disclosure relates generally to efficient feature characterization and/or assessment of a patient's risk for certain clinically significant events based on non- invasive imaging techniques. In one aspect, this disclosure relates to assessment of a patient's risk of suffering a stroke based on multi-contrast-weighing MRI data.
  • a thin fibrous cap covering a large, lipid-rich necrotic core appears to be a clear marker of vulnerable (i.e., high risk) plaque.
  • the "fibrous cap” is a distinct layer of connective tissue that typically covers the lipid core of a plaque deposit.
  • the fibrous cap generally comprises smooth muscle cells in a collagenous-proteoglycan matrix, with varying degrees of infiltration by macrophages and lymphocytes.
  • a thinning fibrous cap indicates weakened structural integrity and possible future rupture that may lead to an embolic event.
  • MRI carotid magnetic resonance imaging
  • a scoring system is used to summarize key factors of atherosclerotic plaque vulnerability into a quantitative number that describes the current status of the lesion and is directly linked to risk of causing clinical events and/or rapid progression of the disease.
  • This scoring approach accounts for juxtaluminal characteristics of atherosclerotic plaque including the status of the fibrous cap and the presence of any or all main plaque tissue components such as hemorrhage, lipid rich necrotic core, and calcification, as well as inflammatory activity, and their relative distance to the vessel lumen.
  • This plaque information is non-invasively acquired in vivo, for example, using MRI.
  • a primary application of the atherosclerotic risk scoring can be found in the clinical diagnosis of human carotid atherosclerosis.
  • one or more cross-sectional images of an artery are taken, for example, by magnetic resonance imaging, computed tomography, ultrasonics, positron emission tomography, or the like, including possibly using combinations of one or more of these imaging modalities.
  • the image is also analyzed to determine the status and composition of the fibrous cap.
  • the fibrous cap may be collagen or mixed tissue (sometimes referred to as "loose matrix") and may be intact or ruptured.
  • An atherosclerotic risk score is then calculated that characterizes the risk associated with the imaged portion of the artery that is dependent on the fibrous cap status and composition and the present of the identified components in the juxtaluminal region of the artery.
  • a deterministic method can be used for delineating plaque components, such as necrotic cores and regions of calcification.
  • a deterministic method can be used for delineating plaque components, such as necrotic cores and regions of calcification.
  • UI user interface
  • Example Regions-of-Interest The present disclosure describes methods and systems for plaque feature characterization and/or risk assessment based on image data obtained from certain regions-of-interest ("ROIs").
  • ROIs regions-of-interest
  • image data such as MRI data
  • MRI data are obtained from the carotid artery and analyzed to assess the patient's risk for stroke.
  • FIG. 1 schematically shows a portion of a carotid artery
  • FIG. 100 showing the bifurcation of the common carotid artery 102 into the internal carotid artery 104 and the external carotid artery 106.
  • Figure 2 schematically shows an exemplary MRI image taken through a cross-section of the external carotid artery 106 at section 2-2 of Figure 1.
  • Figure 2 is a simplified depiction of a high-resolution MRI image, presented here to facilitate understanding of the present invention.
  • a clinician or other healthcare professional may examine more than one image to identify specific features of the atherosclerotic deposit.
  • a skilled clinician can identify in the MRI image(s) the artery 106, outer wall 110, the atherosclerotic plaque 115 therein, and other components of the plaque 115, as discussed below.
  • a computer running image analysis software may be used to identify or facilitate identification of these components.
  • the atherosclerotic plaque 115 is substantial.
  • a lumen 112 provides a flow path for the blood and a relatively narrow fibrous cap 114 forms the interface between the lumen 112 blood flow and the rest of the plaque deposit 115.
  • the fibrous cap 114 may be ruptured, as indicated at 113, which may appear in the MRI image as a light or a dark area on the fibrous cap 114.
  • the plaque 115 may include one or more regions of calcification 116 (two shown), one or more necrotic core region(s) 118 and/or hemorrhage(s) 119.
  • the location of early or recent hemorrhage 119, necrotic core 118, and calcification 116 can also be identified from the MRI image(s) ⁇ in particular, the radial position with respect to the lumen 112.
  • a juxtaluminal region can be identified, as indicated by the dotted line 120, to determine if these components are partially or wholly within the juxtaluminal portion of the plaque deposit 115.
  • a feature characterization and/or risk assessment system 300 includes a local computing device 400, to be described in more detail with further reference to Figure 4.
  • the computing device 400 can be operatively connected to other electronic devices, such as a local imaging device (e.g. MRI scanner, computed tomographic ("CT") scanner, ultrasound scanner, positron emission tomography (“PET”) scanner, and the like).
  • the local computing device 400 can also be operatively connected to one or more remote electronic devices via a network 320.
  • the remote electronic devices can include, for example, a remote computing device 330, which, in turn, can operate a remote imaging device.
  • imaging device is any device capable of generating signals susceptible to being processed to produce position-dependent data, whether the device itself produces actual visual images.
  • an example computing device 400 in one configuration, includes at least one processing unit 402 and a system memory 404.
  • system memory 404 may comprise, but is not limited to, volatile (e.g. random access memory (RAM)), non- volatile (e.g. read-only memory (ROM)), flash memory, or any combination.
  • System memory 404 may include operating system 405 suitable for controlling computing device 400's operation, one or more programming modules 406, and may include a program data 407.
  • programming modules 406 can include, for example, feature characterization and/or risk assessment application, also called analysis application 420.
  • the computing device 400 becomes configured as a special-purpose computing device for feature
  • example processes of this disclosure can be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in Figure 4 by those components within a dashed line 408.
  • Computing device 400 can have additional features or functionality.
  • computing device 400 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape.
  • additional storage is illustrated in Figure 4 by a removable storage 409 and a non-removable storage 410.
  • Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.
  • System memory 404, removable storage 409, and non-removable storage 410 are all computer storage media examples (i.e.
  • Computer storage media can include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 400. Any such computer storage media may be part of device 400.
  • Computing device 400 may also have input device(s) 412 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc.
  • Output device(s) 414 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others can be used.
  • Computing device 400 can also contain a communication connection 416 that allow device 400 to communicate with other computing devices 418, such as over a network (e.g. network 320) in a distributed computing environment, for example, an intranet or the Internet.
  • Communication connection 416 is one example of communication media.
  • Communication media can typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media.
  • modulated data signal may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal.
  • communication media can include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
  • wired media such as a wired network or direct-wired connection
  • wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.
  • RF radio frequency
  • computer readable media may include both storage media and communication media.
  • program modules 406 can perform processes including, for example, one or more of the steps of risk assessment, as described below.
  • Other programming modules that can be used in accordance with aspects of this disclosure can include word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or other computer-aided application programs, etc.
  • the plaque feature characterization and/or risk assessment application 420 includes an image analysis software toolset that facilitates quantitative analysis of blood vessel MRI data sets through semiautomatic or manual contouring and labeling of structures within a user-selected region of interest.
  • the plaque feature characterization and/or risk assessment application 420 includes a user interface designed to follow prescribed clinical workflow patterns to process, review, validate/edit and analyze digital images.
  • the image data which the plaque feature characterization and/or risk assessment application 420 acts upon can be in any suitable format.
  • the image data can include one or more MRI series in the Digital Imaging and Communications in Medicine (“DICOM”) format.
  • the image data can be accessed at any suitable location, including a memory in the computing device 400 itself or one or more of the electronic devices operatively connected to the computing system 400.
  • a process 500 to analyze the image data to assess a patient's risk for a clinically significant event suehras-stroke the user first selects a specific patient and exam for analysis (510).
  • DICOM headers of the images are read to determine which images meet the analysis requirements (based on MRI scan parameters set at the time of imaging).
  • the images that meet the requirements are classified according to patient, date of exam, and contrast weighting (e.g. Tl -weighted ("T1W”), T2-weighted (“T2W”), time-of- flight weighted (“TOF”), and /or proton density weighted (“PDW”) ).
  • T1W T1W
  • T2-weighted T2-weighted
  • TOF time-of- flight weighted
  • PDW proton density weighted
  • the user selects which contrast weightings are to be included in the analysis, sets the longitudinal extent (number of slices) of the analysis, and establishes the longitudinal alignment of the images by selecting the location of a common landmark (e.g. the carotid artery bifurcation (e.g., B in Figure 6(a) and 710 in Figure 7)) in all series and locks all series.
  • a common landmark e.g. the carotid artery bifurcation (e.g., B in Figure 6(a) and 710 in Figure 7)
  • a common landmark e.g. the carotid artery bifurcation (e.g., B in Figure 6(a) and 710 in Figure 7)
  • a common landmark e.g. the carotid artery bifurcation (e.g., B in Figure 6(a) and 710 in Figure 7)
  • multiple images, one from each contrast weighing series are simultaneously displayed on a display device, such as a computer monitor, as shown in Figure 8.
  • the user can issue a command (e.g., by clicking on a button in the graphical user interface of the plaque feature characterization and/or risk assessment application 420) to cause two or more of the series to shift longitudinally in synchronization with each other when one of the series is moved.
  • a command e.g., by clicking on a button in the graphical user interface of the plaque feature characterization and/or risk assessment application 420
  • the user can designate one of the series (e.g., the T1W series, Figure 8(a)) is as the primary series.
  • the plaque feature characterization and/or risk assessment application 420 is configured such that before the locking, changing the vertical location of displayed image in the primary series will cause all series change in sync as if locked, while changing the vertical location of displayed image in a non- primary series will not cause the other series to change in display; after locking, moving any one of the displayed images will cause the rest of the images to change in sync.
  • two or more image series having no common image plane can be used together.
  • the series 610 solid lines in Figure 6(a)
  • a first contrast weighing e.g., Tl W
  • a second contrast weighing e.g., TOF
  • calculations from the image data of the second series can be carried out by the plaque feature
  • characterization and/or risk assessment application 420 to generate a set of interpolated images 630 (dashed lines in Figure 6(b)) such that at least a subset (D, E and F) of the interpolated images of the second contrast weighing can be
  • interpolated slices can be generated from the three-dimensional image data, which is the combined two-dimensional image data from two or more slices.
  • the user is guided to the next activity in analysis with the plaque feature characterization and/or risk assessment application 420: Delineating the vessel lumen and outer wall boundaries in each serial, cross-sectional slice (520). Delineation of these boundaries can be accomplished either with manual drawing tools or with semiautomatic boundary delineation tools, as described in more detail below, using the plaque feature characterization and/or risk assessment application 420. Either method permits manual editing of the results.
  • ROI region of interest
  • the user can delineate the lumen and outer wall boundaries of the vessel in each cross-sectional location for one chosen contrast weighting (the primary series).
  • the user may identify the lumen boundary either by placing a seed point ("*" in Figure 8(a)) inside the lumen or by placing a set of at least 4 seed points along the lumen boundary.
  • boundary delineation algorithms automatically delineate the optimal closed contour corresponding to the lumen.
  • a user input i.e., locations of the seed or seeds, in addition to the image data, is used by the algorithm to calculate the boundary delineate the feature of interest.
  • An example boundary delineation algorithm is described in Paragios N., Deriche R.
  • a lumen boundary identified at one location may also be used to identify lumen boundaries at adjacent locations. Once the lumen boundary has been identified, the user delineates the outer wall boundary in the same contrast weighting using either a semi-automated delineation algorithm or by placing at least 4 seed points along the boundary (arrows in Figure 8(a)). In either case, boundary delineation algorithms automatically delineate the optimal closed contour corresponding to the outer wall. The user may review the result and manually adjust this result.
  • a wall boundary identified at one location may also be used to identify lumen boundaries at adjacent locations.
  • an automatic algorithm for image registration automatically aligns the contours drawn on one contrast weighting with features visible in all other contrast weightings in the analysis. The results are reviewed by the user and remaining misalignments are addressed either by manual adjustment of the misalignment and/or manual adjustment of the contours to better match the image features.
  • the user can also delineate and label the internal structures of the vessel wall (between the lumen and outer wall contours) using either manual drawing and labeling techniques or semi-automatic contours delineation and labeling algorithms.
  • semi-automatic plaque contours delineation algorithm is disclosed in U.S. Patent Application No.
  • a delineation algorithm (see below) automatically delineates regions consisting of calcified and soft (non-calcified) plaque. Manual drawing of calcified and soft plaque regions can also be performed.
  • the software can also highlight the region between soft plaque (or lipid-rich necrotic core) contours and the lumen contour and provide area and thickness measurements of this region, referred to as the fibrous cap or the cap.
  • the user can view rendered images (for example, using maximum intensity projection reformat) of the MR images and three-dimensional renderings of the delineated regions. These rendering methods are standard in the industry.
  • the plaque feature characterization and/or risk assessment application 420 generates one or more reports, which can include the information, either in summary or for each location, derived from the user generated contours. Such information can include one or more of the following:
  • Length of artery segment Total wall area, Maximum wall thickness, Total volumes of all identified regions, by type, User specified cross-sectional or rendering results,
  • the report can be saved in any suitable format, including PDF, CSV, DICOM, and XML file formats.
  • analysis results may be saved to a file that can be reloaded (restored) for further editing or review.
  • Analyzing atherosclerotic plaque consists of multiple complex procedures and requires training. To ensure the average user can consistently obtain a high quality result, a Workflow enforced process, such as the one discussed above, is used to force the user to conduct analysis in an optimal sequence pre-designed by experienced users.
  • a principal feature of the software will be a streamlined user interface that guides the users through a set sequence of intuitive steps to complete the analysis.
  • Each step will permit only specified activities to be performed.
  • a validation check will be made to ensure that all analysis steps meet pre- specified constraints.
  • the user must specify at least one series for analysis, corresponding images must exist for all chosen series, and a landmark location must be specified and series must be locked.
  • One embodiment for validating process in 520, 530,540 is that before proceeding to next step, one lumen contour and one wall contour must exist for each location, the lumen contour must be wholly contained within the wall contour, all other contours must be contained between the wall contour and the lumen contour At any point in the analysis, the user is able to save the results and capture the workflow status in a file, which can be restored at a later time. The analysis can be continued or modified.
  • the plaque feature characterization and/or risk assessment application 420 provides a set of automated algorithms to assist the user in completing the analysis. A description of an example of each of the algorithms is set forth below.
  • the plaque feature characterization and/or risk assessment application 420 utilizes one or more of the following automated algorithms:
  • Carotid plaque composition differs between ethno-racial groups: an MRI pilot study comparing mainland Chinese and American Caucasian patients. Arterioscler Thromb Vase Biol 2005b; 25:611-6.
  • the plaque feature characterization and/or risk assessment application 420 uses B-splines to define the lumen boundary (Kerwin 2007).
  • B- splines are widely used to define closed curves (for example in Microsoft Powerpoint).
  • the resulting contours can be easily modified by manually dragging the control points of the B-spline.
  • plaque feature characterization and/or risk assessment application 420 uses active contour
  • the plaque feature characterization and/or risk assessment application 420 specifically uses a type of B- spline snake described in (Brigger, 2000). The snake seeks to minimize an "energy" function, where the energy is high when the contour is not aligned with a boundary and low when it is aligned.
  • the plaque feature characterization and/or risk assessment application 420's snake begins with a series of initial control points (for example from manual input) that define an initial contour, with an associated energy. The final contour is obtained by modifying the control points using gradient descent until a minimum energy is reached.
  • Mean-shift segmentation In addition to manually identifying the initial control points, the plaque feature characterization and/or risk assessment application 420 can also automatically generate initial control points based on a single click of the mouse within the lumen. This is done using a standard "region growing" approach to identify a region with similar intensity to the selected point.
  • region growing approach is the "mean shift,” as described in Fukunaga (1975). This process iteratively identifies all points that share a common mean intensity. The boundary of this region is used to initialize the B-spline snake.
  • the plaque feature characterization and/or risk assessment application 420 also features the ability to automatically use a lumen contour from a prior image in finding the next. This is done simply by taking the central point of the prior lumen contour and using it in the mean-shift algorithm described above.
  • This approach allows two mechanisms for rapid user adjustment of the results. First, a threshold in the mean shift segmentation can be adjusted to make the range of values accepted within a common mean lower or higher. Second, the B-spline snake result can be quickly adjusted by moving the control points in the B-spline.
  • the outer wall boundary is delineated using the same B- spline snake as described above for the lumen contour.
  • the wall delineation algorithm can be initialized by user input of control points.
  • Lumen Expansion In one aspect of this disclosure, if a user chooses not to enter control points to generate a contour, an automated algorithm can be used to initialize the B-spline contour for the wall. This algorithm cannot rely on mean shift segmentation (as for the lumen) because the outer wall boundary can have diverse brightness levels depending on its makeup. Therefore, the plaque feature
  • characterization and/or risk assessment application 420 uses an approach based on expanding the lumen contour outward. Using a series of increasing outward expansions, the lumen is expanded and then mapped to the closest ellipse. Each ellipse is used to initialize a B-spline snake and the one that produces the overall minimum energy is selected. If the prior location has an outer wall contour, the amount of expansion is proportional to the local thickness on the previous location using a conditional shape model, which is described in, e.g., U.S. Patent Application No. 11/690,063, filed March 22, 2007 and published as U.S. Patent Application Publication 2007/0269086 Al, which application is incorporated herein by reference.
  • the plaque feature characterization and/or risk assessment application 420 can automatically compute an in-plane shift in one example (Kerwin 2007).
  • the shift is determined by a search over all possible shifts (within a user-specified limit) that find the one that best aligns the existing lumen and outer wall contours with the features in the image.
  • the optimal shift is determined as the one that minimizes an energy function proportional to the total gradient of the image intensity beneath the lumen and wall contours (i.e., the line integral of the image gradient). This function is minimized when the contours overly edges apparent within the images.
  • the plaque feature characterization and/or risk assessment application 420 can also provide a user-assisted method within this same framework in which the user drags the image to obtain a rough alignment of the contours with the features. Then the plaque feature characterization and/or risk assessment application 420 identifies the optimal shift within a small window around this point using the algorithm described above.
  • U.S. Patent Application Serial No. 11/445,510 discloses an algorithm of automated in vivo segmentation of atherosclerotic plaque MRI with morphology-enhanced probability maps. This is a statistical based analysis method, where the statistical modeling is captured by the so called probability maps.
  • the probability maps are not a priori knowledge, and therefore have to be developed from a set of statistical training data, the data whose outcomes (analysis results) are known.
  • statistical training data are obtained from subjects having certain characteristic that are expected to be similar to the characteristics to be ascertained from the patients. Based on the training data, the probability maps are derived by best fitting the outcomes of training data.
  • Morphology-enhanced segmentation algorithm for plaque delineation is a general-purpose segmentation algorithm that is based on a simple mathematical model. This algorithm is tailored for plaque delineation by customizing a few parameters of the algorithm based on accepted practices in the medical literature and performance testing on several cases of vessel wall MRI.
  • the general approach of the segmentation algorithm is to assign a "score" to each pixel in the image that indicates how well the pixel matches pre-specified characteristics in terms of intensity and location of the pixel. A high score indicates that the pixel closely matches the characteristics and a low score indicates that the pixel does not match.
  • the difference of its intensity from a desired intensity is computed.
  • the difference is computed by normalizing to the local median intensity. Also, because multiple contrast weightings are used, the total difference for a given pixel is computed as the root- mean-square of all the individual differences. Then, a score is assigned based on the following plot:
  • the height (h) is the maximum score
  • the width (w) is the maximum difference, beyond which the score is 0. This is similar to thresholding except the threshold is "soft” rather than “hard.” In traditional thresholding, the curve would be a step function.
  • a morphology score is also used to provide a "buffer" zone near the lumen and wall contours, where plaque components are unlikely to be found. This factor is determined by the minimum of the distance from the pixel to the lumen and wall boundaries according to the following chart:
  • This factor is multiplied by the intensity score to compute the final score for each pixel. Below the distance threshold (D), the overall score is reduced, whereas above D, the overall score is the same as the intensity score.
  • the basic segmentation framework of the plaque feature characterization and/or risk assessment application 420 allows up to four sets of intensity and location characteristics to be specified with corresponding labels, essentially generating four scores for each pixel.
  • the default configuration only uses two sets of pre-specified characteristics: one for calcified plaque (CA) and one for soft (non-calcified) plaque (SP).
  • the plaque feature characterization and/or risk assessment application 420 is configured to give results that are consistent with the well-validated findings in the relevant medical literature.
  • a number of papers (Saam 2005; Cai 2005; Trivedi 2004; Mitsumori 2003; Moody 2003; Chu 2005; Yuan 2001; Shinnar 1999) have described rules for identifying plaque components according to relative intensity characteristics (hypointense, isointense, or hyperintense) within different contrast weightings (T1W, T2W, PDW, etc.). These techniques have relied on manual delineation of regions that match the indicated intensity characteristics.
  • calcified plaque has been characterized by absence of signal in MRI due to a lack of hydrogen nuclei and susceptibility effects of the calcified deposits.
  • Soft plaque regions are areas of the plaque wherein the soft, non-calcified components have been deposited. These regions generally consist of lipids, cholesterol, necrotic debris, and blood products (hemorrhage). These components generally lead to shortening of Tl and T2 values and hence isointense to
  • the plaque feature characterization and/or risk assessment application 420 sets the desired intensity for calcified plaque to equal 0.5 times the median (hypointense) in both Tl -weighted and T2 -weighted images.
  • the desired intensity for soft plaque is set to equal 1.5 times the median (hyperintense) in Tl -weighted images and to 1.0 times the median (isointense) in T2-weighted images.
  • the width of the ramp function for the intensity score (w) is set to equal 1.0 times the median.
  • the optimal peak values were found to be 21 for calcified plaque and 13 for soft plaque based on testing on a number of test cases.
  • the optimal value of D was found to be 1.5 mm, which corresponds to typical normal thicknesses of large vessel walls.
  • Figure 9 shows an example of the deterministic segmentation algorithm applied to phantom images with three contrast weightings (top row) according to one aspect of the disclosure.
  • the intensity score for calcification shows a bright spot corresponding to the dark region in all contrast weightings.
  • the intensity score for core shows a bright spot corresponding to the region that is bright on Tl W and relatively dark on T2W.
  • morphology score (middle column)
  • Competing Active Contours After the scores for each pixel are determined, for ease of editing, it is desirable to delineate the regions of high scores by contours. For this purpose, plaque feature characterization and/or risk assessment application 420 again utilizes a standard snake algorithm. And, to ensure that contoured regions do not overlap, the method of "competing active contours" (Paragios 2000; Liu 2006) is used.
  • Every point along one contour is matched to a corresponding point on the other contour.
  • the result is a set of triangles with points on the contours as vertices.
  • the set of contours that maximizes the minimum angle over all triangles in the set is defined as optimal. Delaunay triangulation theory states that this set is unique and provides tools for finding the optimum. Once the optimal set of triangles is found, the lengths of lines connecting inner and outer contours are taken to be the local thicknesses, vi.
  • the algorithms used in the semi-automatic tools described above for delineating the lumen and outer walls and plaque components are designed to perform operations automatically.
  • characterization and/or risk assessment application 420 allows the user choose to use a manual operation at any time and not use the corresponding semi-automated tools.
  • the contours generated by the semi-automated tools described above, as well as contours generated manually without using the semi-automated tools are stored separately, and not embedded in, the source images.
  • the contours can thus be modified or deleted without affecting the original image. This applies even after an editing review session has been saved to a project. Upon re-opening the project, the contours are as the user left them, and can be modified without affecting the original images.
  • a further aspect of the present disclosure relates to assessing the risk of a clinically significant event by multiple levels of risk assessment.
  • a common technique currently used to assess stroke risk is stenosis measurement by techniques such as duplex ultrasound imaging, CT angiography (“CTA”), MR angiography (“MRA”) or X-ray angiography.
  • CTA CT angiography
  • MRA MR angiography
  • X-ray angiography X-ray angiography.
  • Patients identified as having severe stenosis are considered high risk and are candidates for surgical intervention (such as stent implantation or carotid endarterectomy (“CEA”)), whereas those identified as having moderate stenosis (for example, 50%-79%) could be considered intermediate risk and are candidates for drug treatment (such as with cholesterol-lowering drugs), if they don't have stroke related symptom.
  • a patient may be at risk for stroke even though the patient does not have severe stenosis. It therefore can be beneficial to conduct a second screening of the patients with moderate levels of stenosis to identify those at high stroke risk for appropriate intervention such as surgery.
  • the second screening can be conducted using a scoring method and system such as those disclosed in U.S. Patent No. 7,340,083 or in U.S. Provisional Patent Application Serial No. 61/184,700.
  • the system can be a computerized system with a risk assessment application such as disclosed in this disclosure.
  • the aforementioned risk scoring method and system can be used to provide further levels of screening after one of following groups is identified:
  • Asymptomatic group with moderate stenosis measured by ultrasound, CTA, MRA, or X-ray Angiography
  • a method and system for efficient assessment of a patient's risk for certain clinically significant events have been described.
  • the deterministic method and the computerized system for running the method provide efficient characterization of plaque component, thereby improving the efficiency of risk scoring.
  • the user interface of the computerized system described herein provides efficient representation and analysis of image data, and provides guidance for the user to following an optimized sequence of steps in risk analysis.
  • a combination of traditional risk assessment method and the scoring system and method, whether or not employing the user interface or deterministic delineation algorithm described above provides added precision of risk prediction in an efficient manner.

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Abstract

L'invention concerne un procédé et un système de caractérisation in vivo de signes de lésions. À l'aide d'un appareil non invasif d'imagerie médicale, une image d'une région intérieure du corps d'un patient est obtenue. La région intérieure peut comprendre des composants d'un signe de lésion (par ex. des plaques) figurant dans une liste de composants. Les composants du signe de lésion sont identifiés en classifiant chaque point de l'image comme correspondant ou non à l'un des composants de signe de lésion figurant dans la liste de composants, en utilisant des informations d'intensité de l'image et des informations de morphologie de l'image, une première relation (telle qu'un score d'intensité) corrélant des informations d'intensité de l'image avec les composants figurant dans la liste de composants et une deuxième relation (telle qu'un score de morphologie) corrélant des informations de morphologie de l'image avec les composants figurant dans la liste de composants. En outre, diverses caractéristiques du signe de lésion sont tirées du résultat de la classification.
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